Sequential Social Network Data

Psychometrika, Vol. 53, No. 2, 261-282, June 1988

22 Pages Posted: 21 Nov 2015 Last revised: 19 Feb 2016

See all articles by Stanley Wasserman

Stanley Wasserman

Indiana University Bloomington

Dawn Iacobucci

Vanderbilt University - Marketing; Vanderbilt University - Marketing

Date Written: 1988

Abstract

A new method is proposed for the statistical analysis of dyadic social interaction data measured over time. The data to be studied are assumed to be realizations of a social network of a fixed set of actors interacting on a single relation. The method is based on loglinear models for the probabilities for various dyad (or actor pair) states and generalizes the statistical methods proposed by Holland and Leinhardt (1981), Fienberg, Meyer, & Wasserman (1985), and Wasserman (1987) for social network data. Two statistical models are described: the first is an "associative" approach that allows for the study of how the network has changed over time; the second is a "predictive" approach that permits the researcher to model one time point as a function of previous time points. These approaches are briefly contrasted with earlier methods for the sequential analysis of social networks and are illustrated with an example of longitudinal sociometric data.

Keywords: sociometric measurement, sequential dyadic interactions, sociometry, multivariate directed graph, log linear model

Suggested Citation

Wasserman, Stanley and Iacobucci, Dawn and Iacobucci, Dawn, Sequential Social Network Data (1988). Psychometrika, Vol. 53, No. 2, 261-282, June 1988, Available at SSRN: https://ssrn.com/abstract=2692256

Stanley Wasserman

Indiana University Bloomington ( email )

Dept of Biology
100 South Indiana Ave.
Bloomington, IN 47405
United States

Dawn Iacobucci (Contact Author)

Vanderbilt University - Marketing ( email )

Nashville, TN 37203
United States

Vanderbilt University - Marketing ( email )

Nashville, TN 37203
United States

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